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The concept of signal-free management at road junctions is tailored for Connected and Automated Vehicles (CAVs), in which the conventional signal control is replaced by various right-of-way assignment policies. First-Come-First-Served (FCFS) is the most commonly used policy. In most proposed strategies, although the traffic signals are replaced, the organization of vehicle trajectory remains the same as that of traffic lights. As a naturally signal-free strategy, roundabout has not received enough attention. A key motivation of this study is to theoretically compare the performance of signalized intersection (I-Signal), intersection using FCFS policy (I-FCFS), roundabout using the typical major-minor priority pattern (R-MM), and roundabout adopting FCFS policy (R-FCFS) under pure CAVs environment. Queueing theory is applied to derive the theoretical formulas of the capacity and average delay of each strategy. M/G/1 model is used to model the three signal-free strategies, while M/M/1/setup model is used to capture the red-and-green light switch nature of signal control. The critical safety time gaps are the main variables and are assumed to be generally distributed in the theoretical derivation. Analytically, I-Signal has the largest capacity benefiting from the ability to separate conflict points in groups, but in some cases it will have higher delay. Among the other three signal-free strategies, R-FCFS has the highest capacity and the least average control delay, indicating that the optimization of signal-free management of CAVs based on roundabout setting is worthy of further study.
Yuanyuan Wu; Feng Zhu. Junction Management for Connected and Automated Vehicles: Intersection or Roundabout? Sustainability 2021, 13, 9482 .
AMA StyleYuanyuan Wu, Feng Zhu. Junction Management for Connected and Automated Vehicles: Intersection or Roundabout? Sustainability. 2021; 13 (16):9482.
Chicago/Turabian StyleYuanyuan Wu; Feng Zhu. 2021. "Junction Management for Connected and Automated Vehicles: Intersection or Roundabout?" Sustainability 13, no. 16: 9482.
The maximum platoon size is a critical parameter in connected and automated vehicle (CAV) platoon configuration. However, the effect of platoon size on the transportation system has not been well-studied. This paper unveils the effect of maximum CAV platoon size in terms of road capacity and traffic flow stability. Specifically, the analytical formulations of the capacity and flow stability are developed considering the maximum platoon size. Simulations are conducted to verify the developed theoretical models. For capacity analysis, both the analytical and simulation results indicate that a larger maximum platoon size can help increase the capacity. However, the increment becomes smaller with the increase of maximum platoon size. For flow stability analysis, the theoretical analysis and microscopic simulation show that smaller maximum platoon size leads to greater traffic flow stabilization. In addition, analysis shows that improvements in capacity and traffic stability are more profound when CAV penetration and platooning intensity are high.
Jiazu Zhou; Feng Zhu. Analytical analysis of the effect of maximum platoon size of connected and automated vehicles. Transportation Research Part C: Emerging Technologies 2020, 122, 102882 .
AMA StyleJiazu Zhou, Feng Zhu. Analytical analysis of the effect of maximum platoon size of connected and automated vehicles. Transportation Research Part C: Emerging Technologies. 2020; 122 ():102882.
Chicago/Turabian StyleJiazu Zhou; Feng Zhu. 2020. "Analytical analysis of the effect of maximum platoon size of connected and automated vehicles." Transportation Research Part C: Emerging Technologies 122, no. : 102882.
Canonical correlation analysis (CCA) is a cost insensitive method. It assumes the same loss for different classification errors and aims to attain a low error rate by maximizing the cross-view correlation. However, in some real-world applications, different classification errors will lead to unequal misclassification losses. In addition, in practice, only limited cost label information is available in training set due to the expensive costs of labelling. This paper aims to perform label propagation with CCA in a unified cost-sensitive learning framework. By learning jointly, both the label propagation and CCA can feed back to each other. Thus, more discriminative and cost-sensitive projections will be learned for feature fusion. We test the proposed method on the cost-sensitive application of door-locker system based on multi-view face recognition. The results in comparison with eight label propagation methods, eleven CCA related methods and eight cost-sensitive single-view methods demonstrate its effectiveness.
Jianwu Wan; Feng Zhu. Cost-Sensitive Canonical Correlation Analysis for Semi-Supervised Multi-View Learning. IEEE Signal Processing Letters 2020, 27, 1330 -1334.
AMA StyleJianwu Wan, Feng Zhu. Cost-Sensitive Canonical Correlation Analysis for Semi-Supervised Multi-View Learning. IEEE Signal Processing Letters. 2020; 27 (99):1330-1334.
Chicago/Turabian StyleJianwu Wan; Feng Zhu. 2020. "Cost-Sensitive Canonical Correlation Analysis for Semi-Supervised Multi-View Learning." IEEE Signal Processing Letters 27, no. 99: 1330-1334.
Traffic flow fundamental diagram (FD) is viewed as the basis of traffic flow theory and has various applications in transportation. However, the fundamental diagram of mixed human-driven vehicles (HVs) and connected automated vehicles (CAVs) traffic has not been well-studied. This paper derives the FD for mixed HV and CAV traffic considering the stochastic headway. Firstly, the deterministic FD of pure CAV traffic and pure HV traffic are built. Then the FD of mixed HV and CAV traffic is developed with CAV penetration and platooning intensity taken into consideration. A Gaussian mixture model (GMM) is applied to model the stochastic headway, based on which the stochastic FD is derived. Impact of CAV penetration and platooning intensity on the stochasticity of FD is studied. Results from theoretical analysis and case study show that increasing CAV penetration can reduce the scattering of FD, while higher platooning intensity may result in more scattering of FD.
Jiazu Zhou; Feng Zhu. Modeling the fundamental diagram of mixed human-driven and connected automated vehicles. Transportation Research Part C: Emerging Technologies 2020, 115, 102614 .
AMA StyleJiazu Zhou, Feng Zhu. Modeling the fundamental diagram of mixed human-driven and connected automated vehicles. Transportation Research Part C: Emerging Technologies. 2020; 115 ():102614.
Chicago/Turabian StyleJiazu Zhou; Feng Zhu. 2020. "Modeling the fundamental diagram of mixed human-driven and connected automated vehicles." Transportation Research Part C: Emerging Technologies 115, no. : 102614.
This paper investigates a bike-way network design problem for retrofitting existing cycling infrastructure for commuter cyclists. A multi-objective integer linear programming model is formulated to determine the spatial layout of bike-way networks and types of bike-way links. The objective of the formulation is to maximize the accessibility, minimize the number of intersections, maximize bicycle level of service, and minimize total construction cost subject to space–time constraint and monetary budget. In the formulation, the accessibility measure considers not only connectivity, but also cyclists’ travel time budget between each origin-activity location pair. The problem is solved by augmented \(\epsilon\)-constraint method to generate a set of non-dominated solutions. Two numerical examples are used to demonstrate the feasibility of the model and solution algorithm. For the hypothetical numerical example based on the bike-way network of Jurong Lake district in Singapore, four alternative non-dominated bike-way design plans are generated.
Siying Zhu; Feng Zhu. Multi-objective bike-way network design problem with space–time accessibility constraint. Transportation 2019, 47, 2479 -2503.
AMA StyleSiying Zhu, Feng Zhu. Multi-objective bike-way network design problem with space–time accessibility constraint. Transportation. 2019; 47 (5):2479-2503.
Chicago/Turabian StyleSiying Zhu; Feng Zhu. 2019. "Multi-objective bike-way network design problem with space–time accessibility constraint." Transportation 47, no. 5: 2479-2503.
Conventional intersection managements, such as signalized intersections, may not necessarily be the optimal strategies when it comes to connected and automated vehicles (CAVs) environment. Autonomous intersection management (AIM) is tailored for CAVs aiming at replacing the conventional traffic control strategies. In this work, using the communication and computation technologies of CAVs, the sequential movements of vehicles through intersections are modelled as multi-agent Markov decision processes (MAMDPs) in which vehicle agents cooperate to minimize intersection delay with collision-free constraints. To handle the huge dimension scale incurred by the nature of multi-agent decision making problems, the state space of CAVs are decomposed into independent part and coordinated part by exploiting the structural properties of the AIM problem, and a decentralized coordination multi-agent learning approach (DCL-AIM) is proposed to solve the problem efficiently by exploiting both global and localized agent coordination needs in AIM. The main feature of the proposed approach is to explicitly identify and dynamically adapt agent coordination needs during the learning process so that the curse of dimensionality and environment nonstationarity problems in multi-agent learning can be alleviated. The effectiveness of the proposed method is demonstrated under a variety of traffic conditions. The comparison analysis is performed between DCL-AIM and the First-Come-First-Serve based AIM (FCFS-AIM), with Longest-Queue-First (LQF-AIM) policy and the signal control based on the Webster’s method (Signal) as benchmarks. Experimental results show that the sequential decisions from DCL-AIM outperform the other control policies.
Yuanyuan Wu; Haipeng Chen; Feng Zhu. DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles. Transportation Research Part C: Emerging Technologies 2019, 103, 246 -260.
AMA StyleYuanyuan Wu, Haipeng Chen, Feng Zhu. DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles. Transportation Research Part C: Emerging Technologies. 2019; 103 ():246-260.
Chicago/Turabian StyleYuanyuan Wu; Haipeng Chen; Feng Zhu. 2019. "DCL-AIM: Decentralized coordination learning of autonomous intersection management for connected and automated vehicles." Transportation Research Part C: Emerging Technologies 103, no. : 246-260.
The simultaneous estimation of crash frequency and severity has been studied for years, but most of the existing methodologies adopt mean regression models to estimate the parameters. This study presents the quantile selection model as a methodological alternative in analyzing crash rate and severity at different levels, focusing on addressing the heterogeneity and endogeneity issues so as to identify the influencing factors at signalized intersections. A two-step estimation procedure is carried out, in which the Heckman selection framework accommodates the endogenous relationship between crash rate and crash severity at different levels, while the quantile regression estimates various quantiles of crash rate instead of the mean regression, and accounts for the heterogeneity attributed to unobserved factors. Compare to the general Heckman selection model, the quantile approach is able to provide more comprehensive information about the impact of the influencing factors on crash rate. The model uses 555 observations from 262 signalized intersections in the Hong Kong metropolitan area, integrated with information on the traffic flow, geometric road design, road environment, traffic control and any crashes that occurred during two years. The proposed model reveals more detailed information in terms of different quantiles and improves the prediction accuracy.
Xuecai Xu; Y. C. Li; Sze Chun Wong; Feng Zhu. A Two-Step quantile selection model for the safety analysis at signalized intersections. Journal of Transportation Safety & Security 2018, 12, 547 -565.
AMA StyleXuecai Xu, Y. C. Li, Sze Chun Wong, Feng Zhu. A Two-Step quantile selection model for the safety analysis at signalized intersections. Journal of Transportation Safety & Security. 2018; 12 (4):547-565.
Chicago/Turabian StyleXuecai Xu; Y. C. Li; Sze Chun Wong; Feng Zhu. 2018. "A Two-Step quantile selection model for the safety analysis at signalized intersections." Journal of Transportation Safety & Security 12, no. 4: 547-565.
The longitudinal control for a platoon of connected and automated vehicles is a popular topic in transportation engineering, nowadays. However, a majority of existing results about the cooperative/distributed platoon control are based on linear models that are derived from nonlinear vehicular dynamics by exact feedback linearization. This nonlinear–linear transformation asks for a complete priori knowledge of vehicular dynamics, which could be difficult to obtain in practice. To overcome this disadvantage and address multiuncertainties including both unknown plant parameters and unknown control coefficients in third-order vehicular node dynamics, this paper proposes a distributed backstepping control scheme in a networked environment. Unknown parameters are identified online, and both internal stability and string stability for constant distance spacing policy are established. Simulation studies are carried out by comparing the adaptive control proposed in this paper with the robust control in the state-of-the-art algorithms.
Yang Zhu; Feng Zhu. Distributed Adaptive Longitudinal Control for Uncertain Third-Order Vehicle Platoon in a Networked Environment. IEEE Transactions on Vehicular Technology 2018, 67, 9183 -9197.
AMA StyleYang Zhu, Feng Zhu. Distributed Adaptive Longitudinal Control for Uncertain Third-Order Vehicle Platoon in a Networked Environment. IEEE Transactions on Vehicular Technology. 2018; 67 (10):9183-9197.
Chicago/Turabian StyleYang Zhu; Feng Zhu. 2018. "Distributed Adaptive Longitudinal Control for Uncertain Third-Order Vehicle Platoon in a Networked Environment." IEEE Transactions on Vehicular Technology 67, no. 10: 9183-9197.
In order to investigate pedestrian injury severity at signalized intersections, a survival analysis-based approach is proposed to analyze injury severity varying with time to identify the influencing factors, while addressing the heterogeneity of unobserved factors at different signalized intersections. The crash data of Las Vegas metropolitan area from 2004 to 2008 is applied, involving 450 signalized intersections with 550 pedestrian crashes. To address the heterogeneity issue due to unobserved factors at different signalized intersections, the Weibull model with gamma heterogeneity is employed and compared with the Weibull model. The comparison indicates that heterogeneity is present in the Weibull survival analysis. The results show that pedestrian crashes, time of day, light conditions, and annual average daily traffic (AADT) on minor arterials are potentially significant factors of increasing the pedestrian injury severity probability. The findings provide useful insights for practitioners and policy makers to improve pedestrian safety at signalized intersections.
Xuecai Xu; Feng Zhu. Pedestrian Injury Severity Analysis at Signalized Intersections: A Survival Analysis Based Approach. CICTP 2018 2018, 1 .
AMA StyleXuecai Xu, Feng Zhu. Pedestrian Injury Severity Analysis at Signalized Intersections: A Survival Analysis Based Approach. CICTP 2018. 2018; ():1.
Chicago/Turabian StyleXuecai Xu; Feng Zhu. 2018. "Pedestrian Injury Severity Analysis at Signalized Intersections: A Survival Analysis Based Approach." CICTP 2018 , no. : 1.